The Soil Conservation Monitorins Information System (SCMIS) presented in this paper is oriented to soil erosion control, resources exploitation, utilization, planning and management for a small watershed (about 10 sq....The Soil Conservation Monitorins Information System (SCMIS) presented in this paper is oriented to soil erosion control, resources exploitation, utilization, planning and management for a small watershed (about 10 sq. km.) on the Loess Plateau. It sums up Remote sensing (RS), Geographical Information System (GIS) and Expert System (ES) and consists of a integrated system. As a basic level information system of Loess Plateau, its perfection and psreading will bring about a great advance in resources exploitation and management of Loess Plateau.展开更多
For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic...For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique(SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The densitybased spatial clustering of applications with noise(DBSCAN) is not rigorous when dealing with the samples near the borderline.We optimize the DBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique(DSMOTE). First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples,different strategies are used to over-sample core samples and borderline samples. Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.展开更多
“慧眼”硬X射线调制望远镜(简称慧眼卫星,英文名为Insight Hard X-ray Modulation Telescope,简称Insight-HXMT或HXMT),于2017年6月15日发射升空,标志着我国自主研发的首个天文台级X射线望远镜的诞生.慧眼卫星凭借其大面积、宽波段、...“慧眼”硬X射线调制望远镜(简称慧眼卫星,英文名为Insight Hard X-ray Modulation Telescope,简称Insight-HXMT或HXMT),于2017年6月15日发射升空,标志着我国自主研发的首个天文台级X射线望远镜的诞生.慧眼卫星凭借其大面积、宽波段、高时间分辨率和高能量分辨率的综合优势,为黑洞与中子星系统的硬X射线快速变化和宽波段能谱研究领域开辟了新的研究窗口.超出设计寿命的慧眼卫星已稳定运行超过8yr,目前状态良好,且有望进一步延长其在轨服务时间.截至2024年10月,慧眼卫星已7次向全球科学界公开征集观测提案,共收到334份有效的观测提案,并据此制定了2368个观测计划.此外,慧眼卫星已向公众发布数据13批次,累计数据量达到40 TB,数据公开比率高达94%.慧眼卫星还向用户提供了不同版本的数据分析软件和标定数据库,在轨标定精度在2%左右,满足科学分析的要求.来自全球17家及国内36家研究机构的学者使用慧眼数据开展了科学研究,发表了约300多篇高质量学术论文,累计引用次数约7300次.展开更多
基金Supported by National Natural Science Foundation of China （61304079, 61125306, 61034002）, the Open Research Project from SKLMCCS （20120106）, the Fundamental Research Funds for the Central Universities （FRF-TP-13-018A）, and the China Postdoctoral Science. Foundation （201_3M_ 5305_27）_ _ _
文摘为有致动器浸透和未知动力学的分离时间的系统的一个班的一个新奇最佳的追踪控制方法在这份报纸被建议。计划基于反复的适应动态编程(自动数据处理) 算法。以便实现控制计划，一个 data-based 标识符首先为未知系统动力学被构造。由介绍 M 网络，稳定的控制的明确的公式被完成。以便消除致动器浸透的效果， nonquadratic 表演功能被介绍，然后一个反复的自动数据处理算法被建立与集中分析完成最佳的追踪控制解决方案。为实现最佳的控制方法，神经网络被用来建立 data-based 标识符，计算性能索引功能，近似最佳的控制政策并且分别地解决稳定的控制。模拟例子被提供验证介绍最佳的追踪的控制计划的有效性。
文摘The Soil Conservation Monitorins Information System (SCMIS) presented in this paper is oriented to soil erosion control, resources exploitation, utilization, planning and management for a small watershed (about 10 sq. km.) on the Loess Plateau. It sums up Remote sensing (RS), Geographical Information System (GIS) and Expert System (ES) and consists of a integrated system. As a basic level information system of Loess Plateau, its perfection and psreading will bring about a great advance in resources exploitation and management of Loess Plateau.
基金supported by the National Key Research and Development Program of China(2018YFB1003700)the Scientific and Technological Support Project(Society)of Jiangsu Province(BE2016776)+2 种基金the“333” project of Jiangsu Province(BRA2017228 BRA2017401)the Talent Project in Six Fields of Jiangsu Province(2015-JNHB-012)
文摘For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique(SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The densitybased spatial clustering of applications with noise(DBSCAN) is not rigorous when dealing with the samples near the borderline.We optimize the DBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique(DSMOTE). First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples,different strategies are used to over-sample core samples and borderline samples. Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.
